Gold rose to 3,780.23 USD/t.oz on September 26, 2025, up 0.81% from the previous day. Over the past month, Gold's price has risen 11.25%, and is up 42.20% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on September of 2025.

Explore insights from Market Research Intellect's Gold Invest Trading Platform Market Report, valued at USD 2.5 billion in 2024, expected to reach USD 5.8 billion by 2033 with a CAGR of 10.2% during 2026-2033.Uncover opportunities across demand patterns, technological innovations, and market leaders.

Gold Fields stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Turkey Gold Market: IGE: TRY: Last Trade Day: Transaction Value data was reported at 2,021,541.500 TRY in Nov 2018. This records a decrease from the previous number of 4,717,195.500 TRY for Oct 2018. Turkey Gold Market: IGE: TRY: Last Trade Day: Transaction Value data is updated monthly, averaging 1,498,180.000 TRY from Jul 1995 (Median) to Nov 2018, with 281 observations. The data reached an all-time high of 171,385,100.000 TRY in Nov 2014 and a record low of 0.000 TRY in Aug 2013. Turkey Gold Market: IGE: TRY: Last Trade Day: Transaction Value data remains active status in CEIC and is reported by Borsa Istanbul . The data is categorized under Global Database’s Turkey – Table TR.Z020: Istanbul Gold Exchange: Gold Market.

This dataset is about books. It has 1 row and is filtered where the book is Trading in gold : how to buy, sell and profit in the market. It features 7 columns including author, publication date, language, and book publisher.

As of June 25, 2024, gold futures contracts to be settled in June 2030 were trading on U.S. markets at around ***** U.S. dollars per troy ounce. This is above the price of ******* U.S. dollars per troy ounce for contracts to be settled in June 2025, indicating that gold traders expect the price of gold to rise over the next five years. Gold futures are contracts that effectively lock in a price for an amount of gold to be purchased at a time in the future, which can then be traded on markets. Futures markets therefore provide an indicator of how investors think a commodities market will develop in the future.

Description for Kaggle Project

Title: Gold Price Prediction

Subtitle: Analysis and Forecasting Using Gold Price Data from Kaggle's goldstock.csv

Description This project aims to analyze and forecast gold prices using a comprehensive dataset spanning from January 19, 2014, to January 22, 2024. The dataset, sourced from Kaggle, includes daily gold prices with key financial metrics such as opening and closing prices, trading volume, and the highest and lowest prices recorded each trading day. Through this project, we perform time series analysis, develop predictive models, formulate and backtest trading strategies, and conduct market sentiment and statistical analyses.

Upload an Image - Choose a relevant image such as a graph of gold price trends, a gold bar, or an illustrative image related to financial data analysis.

Datasets - Source: Kaggle - File: goldstock.csv

Context, Sources, and Inspiration -Context: Understanding the dynamics of gold prices is crucial for investors and financial analysts. This project provides insights into historical price trends and equips users with tools to predict future prices. - Sources: The dataset is sourced from Kaggle and contains historical gold price data obtained from Nasdaq. Inspiration: The inspiration behind this project is to enable researchers, analysts, and data enthusiasts to make informed decisions, develop trading strategies, and contribute to a broader understanding of market behavior.

Gold Invest Trading Platform Market size was valued at USD 1.9 Billion in 2024 and is projected to reach USD 4.2 Billion by 2032, growing at a CAGR of 10.4% during the forecast period 2026-2032.Rising Gold Prices: In 2025, gold prices surged by 26%, reaching record highs of over USD 3,500 per ounce, driving increasing interest in trading platforms for gold investments.Growing Demand for Digital Investment Tools: The popularity of online trading platforms makes gold investment more accessible, allowing a broader range of investors to participate in the market.Geopolitical Uncertainties: Economic instability and geopolitical tensions, such as trade wars or conflicts, increase the appeal of gold as a safe-haven asset, thereby driving demand for gold trading.Expansion of Retail Investors: A growing number of retail investors, supported by user-friendly digital platforms, are engaging in gold trading, fueling market growth.

This dataset provides values for GOLD reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Shams Gold Trading Fze Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SolGold (SOLG): Digging for Gold or a Digger's Folly?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Gold: A Brighter Future Ahead?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The sample data consist of Daily Trading Volumes of 50 Baht Gold Futures,10 Baht Gold Futures, Gold-D, and Gold Online Futures from the period November 5, 2018 to February 27, 2019

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What happens to gold if CPI increases?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Seabridge Gold reported CAD1.58M in Trade Debtors for its fiscal quarter ending in June of 2025. Data for Seabridge Gold | SEA - Trade Debtors including historical, tables and charts were last updated by Trading Economics this last September in 2025.

As of May 2025, the London (morning fixing) price of an ounce of gold cost an average of ******** U.S. dollars, a slight increase compared to the average monthly morning fixing price of ******** U.S. dollars per ounce in the previous month.

London fixing gold price In January 2020, the average price for an ounce of fine gold was ******** U.S. dollars. It increased to ******** U.S. dollars as of April 2022. Although the monthly price for fine gold fluctuates, the average annual price of fine gold is gradually increasing. In 2001, the price for one ounce of gold was *** U.S. dollars, and by 2012 the price had risen to some ***** U.S. dollars. By 2024, the annual average gold price was nearly ***** dollars per ounce. In that year, global gold demand reached ******* metric tons worldwide. Price determinants of fine gold Fine gold is considered to be almost pure gold, where the value of the metal depends on the percentage of fineness. Twenty-four-carat gold is considered fine gold (from 99.9 percent gold by mass and higher). The London Gold Fix acts as a benchmark for the price of gold. The price of gold is set by the members of the London Gold Market Fixing Ltd undertaken by Barclays and its other members. The price is determined twice per business day at 10:30 am and 3:00 pm based on the London bullion market to settle contracts within the bullion market. The price is based on the equilibrium point between supply and demand agreed upon by participating banks. Gold prices must remain flexible, and gold fixing provides an instantaneous price at specified times.

New Gold - Akım değerleri, tarihsel veriler, tahminler, istatistikler, grafikler ve ekonomik takvim - Sep 2025.Data for New Gold including historical, tables and charts were last updated by Trading Economics this last September in 2025.

This statistic shows the leading major financial assets worldwide as of April 30, 2025, by average daily trading volume. Gold had the second-highest average daily trading volume at ****** billion U.S. dollars.

As of the end of April 2024, boerse.de Gold was the best-performing gold exchange-traded commodity (ETC) worldwide. EUWAX Gold followed closely behind in second place, providing an annual return of ***** percent by the month of April.